Biblio

Various critical state models have been developed to understand the hysteresis loss mechanism of high-temperature superconducting (HTSC) films. The analytic relation between the hysteresis loss and the remanent field was obtained based on Bean's critical state model for thin films in the full-penetration case. Furthermore, numerical calculation of local hysteresis loops was carried out by Kim's critical state model. In this paper, we investigated local hysteresis losses for a GdBCO coated conductor by using low-temperature scanning Hall probe microscopy and reproduced the experimental results by applying the critical state model. Because of the demagnetizing effect in thin films, analysis of local hysteresis losses can be useful approach to understand of total hysteresis losses.

In the EPCglobal standards for RFID architecture frameworks and interfaces, the Electronic Product Code Information System (EPCIS) acts as a standard repository storing event and master data that are well suited to Supply Chain Management (SCM) applications. Oliot-EPCIS broadens its scope to a wider range of IoT applications in a scalable and flexible way to store a large amount of heterogeneous data from a variety of sources. However, this expansion poses data security challenge for IoT applications including patients' ownership of events generated in mobile healthcare services. Thus, in this paper we propose Secure-EPCIS to deal with security issues of EPCIS for IoT applications. We have analyzed the requirements for Secure-EPCIS based on real-world scenarios and designed access control model accordingly. Moreover, we have conducted extensive performance comparisons between EPCIS and Secure-EPCIS in terms of response time and throughput, and provide the solution for performance degradation problem in Secure-EPCIS.

Vehicular ad hoc networks (VANETs) are taking more attention from both the academia and the automotive industry due to a rapid development of wireless communication technologies. And with this development, vehicles called connected cars are increasingly being equipped with more sensors, processors, storages, and communication devices as they start to provide both infotainment and safety services through V2X communication. Such increase of vehicles is also related to the rise of security attacks and potential security threats. In a vehicular environment, security is one of the most important issues and it must be addressed before VANETs can be widely deployed. Conventional VANETs have some unique characteristics such as high mobility, dynamic topology, and a short connection time. Since an attacker can launch any unexpected attacks, it is difficult to predict these attacks in advance. To handle this problem, we propose collaborative security attack detection mechanism in a software-defined vehicular networks that uses multi-class support vector machine (SVM) to detect various types of attacks dynamically. We compare our security mechanism to existing distributed approach and present simulation results. The results demonstrate that the proposed security mechanism can effectively identify the types of attacks and achieve a good performance regarding high precision, recall, and accuracy.

A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiment results indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% sensor nodes are misbehaving.